M
Monaldo Mastrolilli
Researcher at Dalle Molle Institute for Artificial Intelligence Research
Publications - 100
Citations - 2831
Monaldo Mastrolilli is an academic researcher from Dalle Molle Institute for Artificial Intelligence Research. The author has contributed to research in topics: Approximation algorithm & Job shop scheduling. The author has an hindex of 23, co-authored 99 publications receiving 2658 citations. Previous affiliations of Monaldo Mastrolilli include SUPSI & University of Lugano.
Papers
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Journal ArticleDOI
Effective Neighborhood Functions for the Flexible Job Shop Problem
TL;DR: A reduction of the set of possible neighbours to a subset for which it can be proved that it always contains the neighbour with the lowest makespan and an efficient approach to compute such a subset of feasible neighbours is presented.
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.
BookDOI
Experimental and Efficient Algorithms
TL;DR: Two theoretically interesting and empirically successful techniques for improving the linear programming approaches, namely graph transformation and local cuts, in the context of the Steiner problem are presented.
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.