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 and metaheuristic 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:
All deadlines are Anywhere on Earth (AoE = UTC-12h).
Types of Submissions
When submitting a paper to LION 20, authors are required to select one of the following three types of papers:
You can submit original and unpublished work either as a long paper (12-15 pages, including references) or short paper (6-11 pages, including references). You can choose to add an appendix.
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 ].
All abstracts (for presentation only) must be submitted using OpenReview at [ Submission ].
Review Process
Please note that the review process for LION 20 is single blind.
Proceedings
Papers accepted into the LION20 proceedings will be published in Lecture Notes in Computer Science (LNCS).
Special Issue
Accepted papers at LION20 will be invited to submit extended versions to a special issue. All manuscripts are subject to peer review. The details of the SI and submissions will be available during LION20.
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. Please address proposals for special sessions to the TPC Chair: prof. Maximilian Schiffer - schiffer(AT)tum.de, with CC: roberto.battiti(AT)unitn.it
Organizers: Lin Xie^1, Yingqian Zhang ^2, and Yaoxin Wu^2 1 Chair of Information Systems and Business Analytics, Brandenburg University of Technology, Germany (email: lin.xie@b-tu.de) 2 Eindhoven University of Technology, Netherlands
Abstract: Large Language Models (LLMs) are emerging as powerful tools for optimization, complementing and extending traditional methods. Their ability to understand problem descriptions, generate heuristics, and support human-in-the-loop decision making opens new possibilities for modeling and solving complex real-world problems. This session invites contributions on how LLMs and related AI techniques can be applied to domains such as logistics, scheduling, energy systems, and finance. Topics of interest include AI-assisted problem formulation, heuristic discovery, end-to-end methods, hybrid approaches that combine LLMs with classical algorithms, and practical applications that demonstrate their impact. Special attention will be given to challenges of scalability, interpretability, and reliability in operational settings. The session aims to bring together researchers and practitioners to explore how LLMs can transform optimization research and practice.
Organizers: Konstantinos Asimakopoulos, Laboratory of Automation and Robotics (LAR), University of Patras, Greece Konstantinos Chatzilygeroudis, Laboratory of Automation and Robotics (LAR), University of Patras, Greece
Abstract: Can optimizers themselves be learned? Traditional solvers must re-solve from scratch every time even within families of very familiar problems. Typically the user needs to hand-design heuristics such as step sizes, branching rules etc. Another interesting way to approach this is to use reinforcement learning (RL) to learn decision rules that guide an optimization process across families of problem instances. In this special session, we invite research where RL discovers update rules, search heuristics, or branching strategies that outperform hand-crafted counterparts on families of problems.
Organizers: Carolina Crespi, Department of Mathematics and Computer Science, University of Catania – carolina.crespi@unict.it Alessio Mezzina, Department of Mathematics and Computer Science, University of Catania – alessio.mezzina@phd.unict.it Mario Pavone, Department of Mathematics and Computer Science, University of Catania – mpavone@dmi.unict.it
Abstract: Dynamic autonomous navigation is a challenging problem at the intersection of optimization, machine learning, and artificial intelligence. Real-world applications, such as robotics, autonomous vehicles, and, in general, exploration of unknown or evolving environments require intelligent agents to adapt in real time to incomplete information, uncertain dynamics, and limited resources. These problems naturally fall under the scope of hard optimization, where classical methods often need to be extended or combined with learning strategies to achieve robust and scalable solutions. This special session focuses on novel models, algorithms, and learning-based approaches to address uncertainty and complexity in autonomous navigation. It welcomes contributions on advanced optimization techniques, machine learning methods for decision-making, and hybrid systems that combine both. The session encourages work that emphasizes experimental methodologies, performance evaluation, and the application of results to real-world case studies. Covering both theoretical developments and practical implementations, it aims to advance the understanding of how optimization and learning can eDectively address the challenges of dynamic, uncertain, and multi-agent navigation scenarios.
Keywords: Optimization under uncertainty, Machine learning for optimization, Dynamic autonomous navigation, Real-time decision-making, Multi-agent systems
Topics of Interest: Relevant topics include, but are not limited to: Learning-based optimization methods for uncertain and dynamic environments, Adaptive and real-time decision-making algorithms, Hybrid approaches combining optimization and machine learning, Reinforcement learning and probabilistic models for navigation tasks, Multi-agent coordination and collective intelligence, Performance evaluation and algorithm selection for complex tasks, Robust and scalable techniques for hard optimization problems, Applications in robotics, transportation, disaster response, and smart infrastructures
Best Paper Awards
The Best Paper Awards at LION20 will recognize outstanding contributions based on originality, technical quality, and impact. Selected papers will be honored during the conference, with each recipient receiving a certificate and a monetary prize.
Francesco Archetti
(Consorzio Milano Ricerche, Italy)
Antonio Candelieri,
University of Milano-Bicocca, Italy
Maximilian
Schiffer
(Professor for Business Analytics & Intelligent Systems, Technical University of Munich,
Germany).
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)
Milan
Hladik, Charles
University, Prague, Czech Republic
Laetitia
Jourdan
(University of Lille, France)
Nikolaos Matsatsinis
(Technical University of Crete, Greece)
Hossein
Moosaei, Jan
Evangelista Purkyně University, Czech Republic
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)
Yingqian Zhang,
Eindhoven University of Technology, The Netherlands.
The LION 20 will be hosted by Universita' degli Studi Milano Bicocca
| Conference fees | ||
|---|---|---|
| Early Registration | Late Registration | |
| Regular | ... Eur | ... Eur |
| Student | ... Eur | ... Eur |
| Accompanying Person | ... Eur | ... Eur |
Fees include: Participation to all sessions, Conference materials, Publication
of accepted
papers in LNCS, Coffee breaks, Lunches, Welcome meeting, Conference dinner, and Social
program.
Accompanying person fee includes: Conference dinner and Social program.
Please contact us by email regarding any queries you may have in relation to the conference or general information.Email: antonio.candelieri@unimib.it