M
Max Manfrin
Researcher at Université libre de Bruxelles
Publications - 13
Citations - 973
Max Manfrin is an academic researcher from Université libre de Bruxelles. The author has contributed to research in topics: Metaheuristic & Ant colony optimization algorithms. The author has an hindex of 8, co-authored 13 publications receiving 952 citations.
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Book ChapterDOI
A comparison of the performance of different metaheuristics on the timetabling problem
Olivia Rossi-Doria,Michael Sampels,Mauro Birattari,Marco Chiarandini,Marco Dorigo,Luca Maria Gambardella,Joshua Knowles,Max Manfrin,Monaldo Mastrolilli,Ben Paechter,Luís Paquete,Thomas Stützle +11 more
TL;DR: In this paper, an unbiased comparison of the performance of straightforward implementations of five different metaheuristics on a university course timetabling problem is presented. And the results show that no metaheuristic is best on all the timetabling instances considered.
Book ChapterDOI
Ant algorithms for the university course timetabling problem with regard to the state-of-the-art
TL;DR: Two ant algorithms solving a simplified version of a typical university course timetabling problem are presented and it is shown that the particular implementation of an ant algorithm has significant influence on the observed algorithm performance.
Journal ArticleDOI
Hybrid Metaheuristics for the Vehicle Routing Problem with Stochastic Demands
Leonora Bianchi,Mauro Birattari,Marco Chiarandini,Max Manfrin,Monaldo Mastrolilli,Luís Paquete,Olivia Rossi-Doria,Tommaso Schiavinotto +7 more
TL;DR: This article analyzes the performance of metaheuristics on the vehicle routing problem with stochastic demands (VRPSD) and explores the hybridization of the metaheuristic by means of two objective functions which are surrogate measures of the exact solution quality.
Journal Article
A comparison of the performance of different metaheuristics on the timetabling problem
Olivia Rossi-Dorial,Michael Sampels,Mauro Birattari,Marco Chiarandini,Marco Dorigo,Luca Maria Gambardella,Joshua Knowles,Max Manfrin,Monaldo Mastrolilli,Ben Paechter,Luís Paquete,Thomas Stützle +11 more
TL;DR: The results show that no metaheuristic is best on all the timetabling instances considered, and underline the difficulty of finding the best metaheuristics even for very restricted classes of timetabling problem.
Book ChapterDOI
Parallel ant colony optimization for the traveling salesman problem
TL;DR: The simplest way of parallelizing the ACO algorithms, based on parallel independent runs, is surprisingly effective; it is given some reasons as to why this is the case.