Call for Papers

The main purpose of the venue 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.

Topics of Interest. Topics related to Operations research, learning and intelligent optimization, including but not limited to:

Important dates

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

Invited talks

Jakub Marecek
(Tenured faculty member at Czech Technical University, Czech Republic)

Title: "Fairness in repeated uses of AI systems"

Abstract: Numerous problems in the sharing-economy platforms (such as Uber, Airbnb, and TaskRabbit) and virtual power plants (such as Tesla Virtual Power Plant in the US and Next Kraftwerke in Europe), can be modeled as multi-agent systems, when suitably generalizing the deterministic discrete-event systems. In the past decade, many such platforms have been deployed at scale, there has been substantial progress in modeling stochastic systems, and there is much interest in regulating such platforms (cf. renewable energy communities and citizen energy communities in the Renewable Energy Directive and the Internal Electricity Market Directive, or the The Digital Markets Act in the EU). According to some opinions, this will yield a new wave of interest in multi-agent systems. Many novel, fundamental questions arise in connection with the deployment of such generalized multi-agent systems in the sharing economy and beyond, which are not only providing decision support, but actually execute actions (“perform actuation”). First, the participants have only partial information about the system and are not perfectly rational.This is well understood in behavioral economics, but has not been considered in many calculi in multi-agent systems. Stochastic aspects have been studied in modeling agents’ behavior in consensus problems, but this approach has yet to be developed for more general settings. Second, in studying the behavior of a multi-agent system, one should consider both the perspective of the aggregate behaviour, and the perspective of the individual participants, which requires a probabilistic formulation of the associated desiderata. Finally, the number of participants changes over time, which limits the direct applicability of results from control theory and game theory. Within multi-agent systems, lack of interest in proving robustness of stochastic models is rooted in the fact that state-space approaches to supervision and verification of modular discrete-event systems are PSPACE-complete even in deterministic calculi, and are undecidable in some stochastic calculi. While space-efficient methods may still be possible for some special cases, radically novel methods are required to manage the state-explosion problem. In a string of recent papers, we have developed guarantees for such stochastic models of multi-agent systems, utilizing non-trivial conditions from non-linear control theory (incremental input-to-state stability), and conditions from applied probability (contractivity on average). These allow for the study of both ergodic properties of the multi-agent systems, and fairness properties from the individual point of view.

Maximilian Schiffer
(Professor for Business Analytics & Intelligent Systems, Technical University of Munich, Germany)

Title: "Optimization-augmented machine learning pipelines"

Abstract : In this talk, we will bridge the gap between combinatorial optimization and machine learning to derive policies for contextual multi-stage decision-making problems that arise in various stochastic problem settings in transportation, control, and supply chain management. We will discuss how to encode effective policies by embedding combinatorial optimization layers into neural networks and training them with decision-aware learning techniques. Specifically, I will provide an overview of the underlying algorithmic pipelines and foundations, and elucidate two paradigms - learning by experience and learning by imitation - to train the pipeline’s statistical models in an end-to-end fashion. I will demonstrate the efficacy of optimization-augmented machine learning pipelines for selected application cases, among others discussing its winning performance on the 2022 EUROMeetsNeurIPS dynamic vehicle routing challenge. Finally, we will put the presented paradigms into perspective and learn how they can also be used to approximate (static) equilibrium problems, focusing on traffic equilibria as a use case.

Carola Doerr
(CNRS research director (DR) at Sorbonne University, Paris, France)

Title: " Automated selection of black-box optimization algorithms"

Abstract : When faced with a new optimization problem, we often lack time, knowledge, or other resources to develop a dedicated algorithm to solve it optimally. In order to nevertheless obtain reasonably good solutions in such settings, we must resort to heuristic approaches. One of the most widely used classes of heuristics are black-box optimization algorithms. Black-box optimization algorithms work in an iterative fashion, alternating between the generation of solution candidates, their evaluation, and adjusting the strategy used to generate the next candidates. A plethora of different black-box optimization strategies exist. Understanding which ones work particularly well for which settings (and why) is a key objective of our research domain. In an ideal world, the selection of a best-suited algorithm would be done automatically and on the fly, i.e., while optimizing the problem. In this presentation, we will discuss recent progress towards this "holy grail" of self-adjusting dynamic black-box optimization algorithms, our theoretical understanding, and the role of machine learning.

Paper submission & Proceedings

Types of Submissions

When submitting a paper to LION 19, authors are required to select one of the following three types of papers:

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.

Submission System

All papers must be submitted using OpenReview at [ Submission ].

Proceedings

Papers accepted into the LION19 proceedings will be published in Lecture Notes in Computer Science (LNCS).

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: (Deep) Reinforcement Learning in OR Optimization

Organizer: Lin Xie^1,2, Jinkyoo Park³, and Yaoxin Wu⁴

¹Chair of Information Systems and Business Analytics, Brandenburg University of Technology, Germany

²University of Twente, Netherlands

³Korea Advanced Institute of Science and Technology (KAIST), Korea

⁴Eindhoven University of Technology, Netherlands

Abstract: The integration of Artificial Intelligence (AI) with Operations Research (OR) is transforming the way complex, large-scale, real-world optimization problems are solved across various industries. This special session will focus on the application of AI techniques, such as reinforcement learning and deep learning, to classical OR challenges in diverse fields, including logistics, transportation, robotics, manufacturing, energy, and healthcare. Topics include AI in combinatorial optimization and multi-agent optimization.

Special session 2: Enhancing Exact Combinatorial Optimization Solvers with Machine Learning

Organizer: Quentin Cappart¹

¹Polytechnique Montréal, Canada

Abstract: Combinatorial optimization finds applications in a wide range of fields, including aerospace, transportation planning, scheduling, and economics. In recent years, there has been growing interest in leveraging machine learning to solve combinatorial problems, either in an end-to-end manner or as a means to enhance existing exact solvers (e.g., integer programming, constraint programming, or SAT solvers). The advantage of integrating machine learning with exact solvers lies in combining the solver's guarantees, such as proving optimality, with the flexibility of learning-based approaches. However, identifying the best methods to incorporate a learning module within a solver remains an open research question. This session focuses on papers that explore machine learning techniques to enhance combinatorial optimization solvers, such as learning how to branch, configuring solvers, pruning the search space, or related innovations.

Special session 3: Sustainability in surrogate models, Bayesian optimization, and parameter tuning

Organizer: Antonio Candelieri¹ Laurens Bliek ²

¹University of Milano-Bicocca, Italy

²Eindhoven University of Technology, Netherlands

Abstract: Nowadays, Artificial Intelligence is known for its potential to solve sustainability-related problems, as well as its negative impact on the climate due to its high energy consumption. At the same time, Bayesian optimization and other surrogate-based algorithms are receiving significant attention for their capability of efficiently solving optimization problems with expensive black-box objectives. This is common in parameter tuning problems in simulation optimization, automated machine learning, and automated algorithm configuration. By replacing the expensive objectives with a surrogate model, computational resources can be saved that would otherwise be allocated to running heavy physics simulators, to train deep learning models, or to run optimization algorithms on multiple problem instances. Therefore, this approach can save time and energy, and reduce emissions. Several challenges remain to properly take sustainability aspects into account with these techniques, such as sustainability measures, energy-aware search strategies, efficient surrogate models, transfer learning, and multi-objective/multi-fidelity optimization approaches. This session focuses on new developments to tackle these challenges, and also on new sustainability-related applications where surrogate-based algorithms such as Bayesian optimization are successfully applied.

Special session 4: Learning and Intelligent Optimization for Physical Systems

Organizer: Konstantinos Chatzilygeroudis ¹,Michael Vrahatis ^1
1University of Patras, Greece

Abstract: Working with physical systems presents unique challenges compared to theoretical models or simulations. Experimentation time must be minimized to avoid costly hardware failures, and algorithms must ensure the safety of both the system and surrounding humans. While data-driven approaches, such as Machine Learning (ML), can learn complex models and improve over time, they require large amounts of data and struggle to provide formal guarantees. Conversely, traditional optimization methods offer stronger theoretical guarantees with less data but lack adaptability for improving performance over time. This special session seeks submissions on "Learning and Intelligent Optimization for Physical Systems," focusing on methods that combine ML and optimization techniques to address real-world challenges. Topics include robot learning, embedded systems, real-time applications, and human-computer interaction.

Program

Organization

General Chairs

Milan Hladik, Charles University, Prague, Czech Republic

Hossein Moosaei, Jan Evangelista Purkyně University, Czech Republic

Local Organizing Committee

• Milan Hladik, Charles University, Prague, Czech Republic
• Hossein Moosaei, Jan Evangelista Purkyně University, Czech Republic
• David Hartman, Charles University, Czech Republic
• Martin Černý, Charles University, Czech Republic
• Elif Garajová, Charles University, Czech Republic
• Matyáš Lorenc, Charles University, Czech Republic
• Jaroslav Horáček, Charles University, Czech Republic
• Petra Příhodová, Charles University, Czech Republic
• Tung Anh Vu, Charles University, Czech Republic

  Technical Program Committee Chair

  Yingqian Zhang, Eindhoven University of Technology, The Netherlands.

  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: (work in progress)

  • Zaharah A. Bukhsh (Eindhoven University of Technology)
  • Francesco Archetti (University of Milano Bicocca)
  • Laurens Bliek (Eindhoven University of Technology)
  • Maurizio Bruglieri (Politecnico di Milano)
  • Mauro Brunato (University of Trento)
  • Sonia Cafieri (Université de Toulouse)
  • Quentin Cappart (Polytechnique Montreal)
  • Renato De Leone (University of Camerino)
  • Luca Di Gaspero (University of Udine)
  • Paola Festa (University of Naples Federico II)
  • Laura Genga (Eindhoven University of Technology)
  • Jerome Geyer-Klingeberg (Universität Augsburg)
  • Pontus Giselsson (Lund University)
  • Isel Grau (Eindhoven University of Technology)
  • Vladimir Grishagin (Nizhny Novgorod State University)
  • Francesca Guerriero (University of Calabria)
  • David Hartman (Charles University)
  • Milan Hladík (Charles University, Prague)
  • Laetitia Jourdan (University of Lille)
  • Serdar Kadıoğlu (Brown University)
  • Marie-Eleonore Kessaci (University of Lille)
  • Michael Khachay (Russian Academy of Sciences)
  • Elias Khalil (University of Toronto)
  • Ahmed Kheiri (Lancaster University)
  • Yury Kochetov (Novosibirsk State University)
  • Ilias Kotsireas (Wilfrid Laurier University)
  • Jan Kronqvist (KTH Royal Institute of Technology)
  • Tomáš Kroupa (Czech Technical University in Prague)
  • Dmitri Kvasov (University of Calabria)
  • Xie Lin (University of Twente)
  • Giusy Macrina (University of Calabria)
  • Jayanta Mandi (KU Leuven)
  • Vittorio Maniezzo (University of Bologna)
  • Silvano Martello (University of Bologna)
  • Laurent Moalic (Université de Haute-Alsace)
  • Hossein Moosaei (Charles University-University of Bojnord)
  • Matsatsinis Nikolaos (Technical University of Crete)
  • Panos M Pardalos (University of Florida)
  • Konstantinos Parsopoulos (University of Ioannina)
  • Tommaso Pastore (University of Naples Federico II)
  • Ornella Pisacane (Università Politecnica delle Marche)
  • Vincenzo Piuri (University of Milan)
  • Miroslav Rada (Prague University of Economics and Business)
  • Michael Römer (Bielefeld University)
  • Frédéric Saubion (Université d'Angers)
  • Andrea Schaerf (University of Udine)
  • Marc Sevaux (Université de Bretagne-Sud)
  • Thomas Stützle (Université Libre de Bruxelles)
  • Tatiana Tchemisova (University of Aveiro)
  • Kevin Tierney (Bielefeld University)
  • Sicco Verwer (Delft University of Technology)
  • Michael Vrahatis (University of Patras)
  • Yaoxin Wu (Eindhoven University of Technology)
  • Dachuan Xu (Beijing University of Technology)
  • Qingfu Zhang (Essex University & City University of Hong Kong)

  Location, travel, accommodation

  Prague

  The LION 19 will be held in a historical building in Malá Strana in the heart of Prague. It is close to many tourist attractions: It is right next to the St. Nicholas Church and on the main tourist road from Prague Castle, across Charles Bridge to Old Town Hall with an Astronomical Clock.

• The building is located at: Malostranské nám. 25, 118 00 Praha 1-Malá Strana. [See Here ]
• For more details about how to get there, see [ Travel Instructions ].

  Visa:

  Keep in mind that the Czech Republic is a member of the European Union and a Schengen country. Then, a valid passport or, for Schengen countries, an identification card is all that is needed for entry into the Czech Republic by citizens of European Union countries. Guests from many countries, including the United States, Canada, Japan, New Zealand, and Iceland, can enter the Czech Republic without a visa. If you need a visa to enter the Czech Republic and attend the LION 19 conference, do not hesitate to contact us to request an official invitation letter.

Contacts