M
Mohand Mezmaz
Researcher at University of Mons
Publications - 38
Citations - 804
Mohand Mezmaz is an academic researcher from University of Mons. The author has contributed to research in topics: Branch and bound & Job shop scheduling. The author has an hindex of 13, co-authored 36 publications receiving 718 citations. Previous affiliations of Mohand Mezmaz include French Institute for Research in Computer Science and Automation & Centre national de la recherche scientifique.
Papers
More filters
Journal ArticleDOI
A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems
Mohand Mezmaz,Nouredine Melab,Y. Kessaci,Young Choon Lee,El-Ghazali Talbi,Albert Y. Zomaya,Daniel Tuyttens +6 more
TL;DR: This work proposes a new parallel bi-objective hybrid genetic algorithm that takes into account, not only makespan, but also energy consumption, and focuses on the island parallel model and the multi-start parallel model.
Proceedings ArticleDOI
A Grid-enabled Branch and Bound Algorithm for Solving Challenging Combinatorial Optimization Problems
TL;DR: An adaptation of the parallel branch and bound algorithm for computational grids based on new ways to efficiently deal with some crucial issues, mainly dynamic adaptive load balancing, fault tolerance, global information sharing and termination detection of the algorithm is proposed.
Journal ArticleDOI
A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem
TL;DR: This work presents a new node decomposition scheme that combines dynamic branching and lower bound refinement strategies in a computationally efficient way and demonstrates that parallel tree search is a key ingredient for the resolution of large problem instances, as strong super-linear speedups can be observed.
Journal ArticleDOI
Combining multi-core and GPU computing for solving combinatorial optimization problems
TL;DR: The design and implementation of Branch-and-Bound (B&B) algorithms for solving large combinatorial optimization problems on GPU-enhanced multi-core machines are revisited and a GPU-accelerated approach in which only a single CPU core is used and only the bounding operator is performed on the GPU device is proposed.
Journal ArticleDOI
Simulation-Based Genetic Algorithm towards an Energy-Efficient Railway Traffic Control
TL;DR: In this paper, a method based on genetic algorithm and empirical single train driving strategies is developed to optimize the train synchronization so as to benefit from the energy regenerated by electronic braking operations.