Research Areas | Intelligent Optimization Lab

Large Language Models for Optimization

Sat, 26 Sep 2026 00:00:00 +0000

IOLab investigates how large language models can complement established combinatorial optimization techniques. Rather than treating an LLM as a stand-alone solver, we study its role as one component of a structured optimization system.

Our work examines LLM-based generative operators within metaheuristics, the ability of models to recall and interpret problem features, and the information encoded in their internal representations. These representations can be evaluated as surrogates for predicting instance difficulty and algorithm behavior in algorithm-selection scenarios.

The group has published a systematic review of LLMs for combinatorial optimization and experimental work on feature extraction and algorithm selection.

Open Research Software and Data

Sat, 26 Sep 2026 00:00:00 +0000

Software and data are research outputs at IOLab. We develop reusable frameworks for implementing optimization algorithms and publish instances, validators, and experimental code so that results can be reproduced and extended.

Our main software projects include EasyLocal++, a C++ framework for local-search algorithms, and JuLES, its Julia counterpart. The group also maintains QuadProg++, a C++ implementation of the Goldfarb-Idnani quadratic-programming algorithm, and OptHub, a web platform for managing optimization instances and solutions.

Recent open datasets cover home healthcare routing and scheduling, periodic waste collection, electric-vehicle routing with nonlinear charging, oven scheduling, and educational timetabling. Source code and data are available through the IOLab GitHub organization.

Optimization Methods

Sat, 26 Sep 2026 00:00:00 +0000

IOLab designs and studies algorithms for complex combinatorial optimization problems. Our core expertise includes local-search metaheuristics such as tabu search, simulated annealing, and large neighborhood search, together with multi-neighborhood and hybrid approaches.

We combine these techniques with constraint programming, mixed-integer programming, and automated algorithm configuration. The goal is not only to obtain high-quality solutions, but also to build methods that are robust, reusable, and suitable for the operational constraints of real applications.

A complementary research line studies how optimization algorithms behave. We use statistical experimental design, instance-space analysis, local-optima networks, and algorithm selection to characterize problem instances and understand which methods work best under different conditions.

These methods support the group’s application work in scheduling and timetabling, healthcare, logistics, industrial production, and decision-support systems.

Healthcare Optimization

Tue, 01 Jun 2021 17:03:33 +0200

IOLab develops optimization methods for coordinating healthcare services and resources while accounting for operational efficiency, quality of care, and the needs of patients and healthcare professionals.

Our work includes patient admission scheduling, where patients are assigned to beds while considering clinical requirements, comfort, emergency arrivals, uncertain lengths of stay, and the possibility of delaying admissions. We also study home healthcare routing and scheduling, jointly planning caregivers’ routes, visits, skills, continuity of care, and patient preferences.

In emergency medicine, the group collaborated with ASUFC within EasyNet to build a data-driven simulator of the Friuli Centrale emergency medical system and optimization models for ambulance location and relocation.

These strands were brought together in IMHO, a PRIN project on models and algorithms for integrated healthcare management. The group publishes benchmark instances, generators, validators, and reference methods through the UHHC repositories.

Logistics

Tue, 01 Jun 2021 17:03:29 +0200

IOLab studies complex routing, transportation, and packing problems arising in operational settings. Our research extends classical models such as the Vehicle Routing Problem to account for heterogeneous fleets, periodic services, intermediate facilities, nonlinear charging, uncertain demand, and other constraints found in practice.

Current work focuses on sustainable waste collection, including the planning of electric-vehicle fleets in collaboration with AcegasApsAmga. The activity combines mathematical models, metaheuristics, and data-driven analysis to design efficient collection plans while reducing environmental impact.

The group also works on transportation and service logistics, including railway capacity estimation for industrial freight junctions and location-routing models for emergency medical services.

Open resources

We publish datasets and validation tools for reproducible research, including instances for periodic waste-collection routing and electric-vehicle routing with nonlinear charging. See the IOLab organization on GitHub and the Smart Waste Collection project.

Educational Timetabling

Tue, 01 Jun 2021 17:02:33 +0200

IOLab has worked on automated timetabling since the group was founded in 2000. We develop models and algorithms for assigning lectures, examinations, and other events to times and rooms while balancing hard operational constraints with the preferences and workload of students and staff.

Our research covers local-search metaheuristics, constraint programming, hybrid methods, benchmark design, and rigorous experimental comparison. Group members have also contributed to the organization of the International Timetabling Competitions and to widely used benchmark datasets for course timetabling.

The methods developed by the group have had practical impact beyond research. EasyLocal++, our open-source framework for local-search algorithms, became the technological foundation of EasyCourse, a commercial university timetabling system developed by EasyStaff.

The same expertise extends to sports timetabling: an IOLab team placed second in the Fifth International Timetabling Competition in 2021.