M
Marcel Mongeau
Researcher at École nationale de l'aviation civile
Publications - 79
Citations - 1700
Marcel Mongeau is an academic researcher from École nationale de l'aviation civile. The author has contributed to research in topics: Air traffic control & Optimization problem. The author has an hindex of 19, co-authored 74 publications receiving 1419 citations. Previous affiliations of Marcel Mongeau include University of Edinburgh & Centre de Recherches Mathématiques.
Papers
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Journal ArticleDOI
Comparison of musical sequences
Marcel Mongeau,David Sankoff +1 more
TL;DR: Concepts from the theory of sequence comparison are adapted to measure the overall similarity or dissimilarity between two musical scores, and a dynamic programming algorithm is presented for calculating the measure and applied to a set of variations on a theme by Mozart.
Journal ArticleDOI
Event-based MILP models for resource-constrained project scheduling problems
TL;DR: A comparative study of several mixed integer linear programming (MILP) formulations for resource-constrained project scheduling problems (RCPSPs) shows that no MILP formulation class dominates the other ones and that a state-of-the art specialized method for the RCPSP is even outperformed by MILP on a set of hard instances.
Book ChapterDOI
Simulated annealing: From basics to applications
TL;DR: This chapter surveys the following practical issues of interest to the user who wishes to implement the SA algorithm for its particular application: finite-time approximation of the theoretical SA, polynomial-time cooling, Markov-chain length, stopping criteria, and simulation-based evaluations.
Journal ArticleDOI
Optimization of aircraft container loading
Marcel Mongeau,Christian Bes +1 more
TL;DR: In this paper, the authors address the problem of loading as much freight as possible in an aircraft while balancing the load in order to minimize fuel consumption and to satisfy stability/safety requirements.
Journal ArticleDOI
Exact Sparse Approximation Problems via Mixed-Integer Programming: Formulations and Computational Performance
TL;DR: Exact l0-norm optimization is shown to outperform classical methods in terms of solution quality, both for over- and underdetermined problems, and to be an efficient alternative, in moderate dimension, to classical (suboptimal) sparse approximation algorithms with l2 data misfit.