See the full program, including invited talks and posters, here.

This list is not a formal publication and is reserved for the LION20 participants; the formal proceedings by Springer Verlag will be issued after the conference, as in the tradition of LION.

Click the paper title to access the PDF file. Conference speaker names are highlighted.

Special Session 2 “Matheuristics”

• Vittorio Maniezzo. Fore-and-Back for Changepoint Detection.
• Abdo Abouelrous, Laurens Bliek, Adriana F. Gabor, Yaoxin Wu1, and Yingqian Zhang. Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application.
• Alberto Santini, Enrico Malaguti, and Paolo Paronuzzi. Algorithms and Complexity Results for the 0-1 Knapsack Problem with Group Fairness.

General Track

• Nathan Amoussou, Denis Pallez, Florent Jaillet, and Jean-Paul Comet. Variable-Length Diverse Planning Monte Carlo Tree Search.
• Carlos Ansótegui and Eduard Torres. A Benchmark Generator for Combinatorial Testing.
• Gabriele Favazzi Mollica and Giovanni Righini. Optimization algorithms for the price clustering problem.
• Alessio Mezzina, Carolina Crespi, Vincenzo Cutello, and Mario Pavone. An Autonomous Navigation Framework for Item Retrieval in Unknown Environments.

Special Session 1 “AI-Driven Optimization: Transforming Optimization with LLMs”

• Henrik Abgaryan, Tristan Cazenave, and Ararat Harutyunyan. Starjob: Dataset for LLM-Driven Job Shop Scheduling.
• Lorenzo Saccucci, Marco Boresta, Alessandro Pannone, Antonio Vicinanza, and Francesco Romito. ELEVATION: Exploiting LLMs to Improve Efficiency, Scalability, and Flexibility of Heuristics in Solving Optimization Problems.
• Abhishek Shukla, Anikeit Khanna, Ankur Sinha, and Faiz Hamid. LiFT: Local Search via Linear Programming for Overfitting-Controlled Transformers.
• Kartik Pandey, Shashank Mishra, and Vijaya J. Budgeted Multi-Objective Feature Engineering via LLM-Guided Program Search.
• Lukas Kinder, Quentin Cappart, and Tobias Käfer. Combining Symbolic Constraints and LLMs: An Application to the Abstraction and Reasoning Corpus.
• Yuliang Song and Eldan Cohen. CP-SynC: Multi-Agent Zero-Shot Constraint Modeling in MiniZinc with Synthesized Checkers.

General Track

• Asbjørn Ebenezer Magnussen, Bissan Ghaddar, and Stefan Røpke. Decompositions for Semidefinite Programming with Reinforcement Learning.
• Feyzullah Kara and Figen Oztoprak. Learning-Based Minimization of Packet Loss in Time-Sensitive Networking.
• Akram Badreddine Laissaoui, Taha Arbaoui, Anne-Laure Ladier, and Khaled Hadj-Hamou. How forecast errors propagate into Sales and Operations Planning decisions: A controlled study of RL robustness.
• Lukas Bierling, Kevin Tierney, and Alena Otto. Optimizing Dynamic Replica Placement in in Wide-Area Replicated Systems via Reinforcement Learning.

Special Session 5 “Advances in Data-Driven Optimization and AI-Enhanced Business Process Mining”

• Shruti Kirti Nandan, Uppili Srinivasan Venkatesan, Aditya Acharya, Varun Jagad, Ranjana Vyas, and Monika Gupta. Towards an Explainable Decision Intelligence: Leveraging Predictive ML, SHAP, and Causal Digital Twin for Order-toCash Process.
• Lucky Banjare and Vijaya J. Multimodal Explainable Wildfire Detection Using YOLOv8 and Transformer Attention.
• Anukriti Tripathi, Anshul Singh, Hrithik Roshan, Abhishek Kumar, Pranav Sahu, Ranjana Vyas, and O P Vyas. OITM: An Object-Type Interaction and Temporal Model for Object-Centric Process Mining.
• Shruti Kirti Nandan, Aamod Vyas, Pranav Sahu, and Ranjana Vyas. Drift-Aware Process Mining for Loan Processing Systems: A Framework for Detecting and Managing Concept Drift in Digital Lending and Microfinance Operations.

Special Session 8 “Bayesian optimization: recent achievements and challenges ahead”

• Niki Triantafyllou, Andrea Bernardi, and Maria M. Papathanasiou. Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models.
• Antonio Candelieri and Francesco Archetti. Wasserstein-enabled characterization of designs and myopic decisions in Bayesian Optimization.
• Omer Ekmekcioglu, Juergen Branke, and Nursen Aydin. Combining GPs and NNs as Surrogate Models in BO.
• Rahul Patel and Elias B. Khalil. BPO: Bayesian Preference Optimization for Multiobjective Discrete Optimization.

Special Session 5 “Advances in Data-Driven Optimization and AI-Enhanced Business Process Mining”

• Tarang Varshney, Lakshya Garg, Parth Patel, and Satyanarayana Vollala. AdaptSRNet: Enhancing Image Steganalysis via Adaptive Filter-Attention Fusion.
• Shashank Mishra, Aditya Jha, Kartik Pandey, and Vijaya J. Physics-Guided Optimization for Learning under Physiological Constraints: A Case Study on Hemoglobin Estimation.
• Alla Abhiram, G.S. Abhinav, Nenavathu Pranay, Kislay Dilliwar, and Vijaya J. CAL-ATS: Cosine Adaptive Lookahead Optimization for Automated Applicant Tracking Systems.
• Pranav Sahu, Avinash Singh, Uphar Singh, and Ranjana Vyas. TuroMiner : An efficient and advanced process discovery algorithm for complex processes.

Keynote and poster sessions only plus social events; no track sessions. See the full program for details.

General Track

• Mohsen Nafar, Michael Römer, and Lin Xie. A Clustering-Based Variable Ordering Framework for Relaxed Decision Diagrams for Maximum Weighted Independent Set Problem.
• Gloria Giorgetti, Simone Bianco, Pietro Firpo, Riccardo Berta, and Danilo Pau. Practical Visual Question Answering at the Edge.
• Mickaël Basson and Philippe Preux. Leveraging Structural Constraints for Diffusion-based Neural TSP Solvers.
• Sameh Al-Shihabi. Defining Core Problems for Set Covering Instances Using Machine Learning: a Proof of Concept.
• Elias Reich, Saverio Messineo, and Stefan Huber. Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process.
• Syed Amir Hamza and Alexander Jesser. Adaptive Backbone Selection for Complexity-Aware and Energy-Efficient Visual Inference.
• Iman Seyedi and Francesco Archetti. Diffusion-enabled Optimal Transport distances for graph matching.
• William Stenberg, Caroline Granfeldt, Torbjörn Larsson, and Roghayeh Hajizadeh. Optimizing persistent surveillance missions for UAVs by column generation.
• Mick Molitor and Natalia Kliewer. A Preliminary Study on GAN-Augmented Column Generation.
• Efthyvoulos Drousiotis, Shashi Lakra, Matthew Feng, Fahim Razai, Ana L. Carvalho, Paul G. Spirakis, Simon Maskell, and Peter Green. From Ensembles to One Graph: Consensus DAGs with Minimal Information Loss.
• Simon Renard, Quentin Louveaux, and Bernard Fortz. Genetic Branching: An Evolutionary Framework for Interpretable Branching Strategies.
• Kaushal Kumar Ojha, Tara Hemaliya, and Sachchida Nand Chaurasia. An Efficient Greedy-Randomized Heuristic for the Minimum Weakly Connected Dominating Set Problem.
• Mitra Heidari, Mohammad Golzarijalal, Ling Luo, Ellen Otte, and Uwe Aickelin. Multi-fidelity Optimisation via Hybrid Genetic-Greedy Search.
• Kenichi Tamura. Integrated Formulation of Sequential Multiobjective Optimization Based on System Surrogate Model Learning Under 𝐿_∞-norm Error Constraints.
• Henrik Abgaryan, Tristan Cazenave, and Ararat Harutyunyan. Opening the Black Box: Topologically Guided Latent Steering for Neural Combinatorial Optimization.
• Helga Ingimundardóttir, Margrét Vala Þórisdóttir, Bjarki Þór Elvarsson, and Tómas Philip Rúnarsson. A Scalable Matheuristic for Routing Capacity-Constrained Groundfish Surveys.
• Chang Liu and Elias B. Khalil. Vertex Sampling for Backdoor Search in Binary Linear Optimization.
• Juan Sebastián Herrera-Cobo and David Álvarez-Martínez. Heuristic-Based Agent to Solve The Online Three-Dimensional Container Loading Problem.

General Track

• Subhas Mondal, Saptadeep Biswas, Seyedali Mirjalili, Absalom E. Ezugwu, and Uttam Kumar Bera. Quantum-Enhanced Chaotic Particle Swarm Optimization for LSTM-Based Stock Market Forecasting.
• Farzam Salimi, Ana Rita Simões do Paço e Vaz, and António Galrão Ramos. Learning District Visitation Sequences for Hierarchical Last-Mile Delivery.
• Nikolaos Liouliakis, Angelo Sifaleras, and Rachid Benmansour. Constraint programming for unrelated parallel machine scheduling with unrelated servers.
• Md Tabish Haque, Sebastian Sehmisch, Christopher Szymula, and Nikola Bešinović. A Two-Stage Learning-and-Optimization Framework for Real-Time Train Platforming.
• JJ Merelo, Cecilia Merelo-Molina, Pablo García-Sánchez, Mario García-Valdez, and Juan Luis Jiménez-Laredo. Is there a (carbon-) free lunch? Energy/performance tradeoffs in population-based metaheuristics.
• Laura Wolf, Sabrina Klos, and Stefan Nickel. Heuristics for Variable Cost and Size Cluster Vector Bin Packing.
• Oleksandr Yakovenko, Mahdi Mostajabdaveh, Cheikh Ahmed, Abdullah Ali Sivas, Xiaorui Li, Zirui Zhou, and Mao Kun. COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space.
• Cheikh Ahmed, Mahdi Mostajabdaveh, and Zirui Zhou. Latent Heuristic Search: Continuous Optimization for Automated Algorithm Design.