Working Group 1

Problem Modelling and User Experience

Leader: Luca Di Gaspero, IT
Vice-Leader: Teresa Montrone, IT

WG1 focuses on the crucial aspect of translating an informal problem description into a formal model that can be implemented, executed, and optimised by Randomised Optimisation Algorithms (ROAs), and aims at making the deployment of ROAs easier and more accessible to end users in a wide variety of applications.

Tasks

  • Investigating how industrial and academic users apply optimisation algorithms to their problems, whether expressed formally or informally, and surveying and curating examples of model formalism, model-ROA interfaces, and available software libraries. User requirements related to problem modelling, algorithm selection and configuration, workflow integration, result visualisation, preference articulation, and interaction with the optimisation process, will be identified, as well as how practical applications would be enabled (or altered) by a unified black-box optimisation problem-modelling framework.
  • Developing a modelling framework for black-box optimisation problems defined on both numerical and combinatorial decision variables, defining a suitable model-ROA interface, and providing a reference software implementation of that framework.
  • Coordinating and contributing to the development of an open-source library of problem instances, modelling examples, and solvers, and preparing supporting documentation and training materials on problem formulation, modelling, and solution processes.

News

2025-03-27

ROAR-NET API Specification unveiled

The development repository for the ROAR-NET API Specification is now publicly available on GitHub.

2025-01-09

First Call for Young Researcher and Innovator Conference Grant Applications

ROAR-NET invites applications for Young Researcher and Innovator Conference Grants to be submitted at any time until 30 June 2025.

2024-12-23

Second Call for Short-Term Scientific Missions

ROAR-NET invites applications for Short-Term Scientific Missions to be submitted at any time until 15 July 2025.

Publications

Koç, Ç. (2024). A metaheuristic for the maximum coverage facility location problem with drone. OR66 Annual Conference, 198.

Bibtex
@inproceedings{Koc2024metaheuristic,
	address = {Bangor, United Kingdom},
	author = {Ko{\c c}, {\c C}a{\u g}r\i{}},
	booktitle = {OR66 annual conference},
	year = {2024},
	month = {9},
	pages = {198},
	title = {A metaheuristic for the maximum coverage facility location problem with drone},
}

Djukanović, M., Kartelj, A., Eftimov, T., Reixach, J., & Blum, C. (2024). Efficient search algorithms for the restricted longest common subsequence problem. In L. Franco, C. de Mulatier, M. Paszynski, V. V. Krzhizhanovskaya, J. J. Dongarra, & P. M. A. Sloot (Eds.), Computational Science – ICCS 2024 (Vol. 14836, pp. 58–73). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-63775-9_5

Bibtex
@inbook{Djukanovic2024Efficient,
	address = {Cham},
	author = {Djukanovi{\' c}, Marko and Kartelj, Aleksandar and Eftimov, Tome and Reixach, Jaume and Blum, Christian},
	series = {Lecture {Notes} in {Computer} {Science}},
	booktitle = {Computational {Science} -- {ICCS} 2024},
	doi = {10.1007/978-3-031-63775-9_5},
	editor = {Franco, Leonardo and Mulatier, Cl{\' e}lia and Paszynski, Maciej and Krzhizhanovskaya, Valeria V. and Dongarra, Jack J. and Sloot, Peter M. A.},
	isbn = {978-3-031-63775-9},
	year = {2024},
	month = {7},
	pages = {58--73},
	publisher = {Springer Nature Switzerland},
	title = {Efficient search algorithms for the restricted longest common subsequence problem},
	volume = {14836},
}